Publication
High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting mechanical properties, are inherently atomic scale phenomena, particularly during early-stage nucleation and growth. Atomistic modeling using interatomic potentials is a desirable tool for understanding the detailed phenomena involved in precipitation and strengthening, which requires length and time scales far larger than those accessible by first-principles methods. Current interatomic potentials for alloys are not, however, sufficiently accurate for such studies. In this work we will consider new interatomic potentials for the 2xxx (Al-Cu-Mg), 6xxx (Al-Mg-Si), and 7xxx (Al-Zn-Mg-Cu) age-hardenable aluminum alloys.
First a family of neural-network potentials (NNPs) for the Al-Cu system are presented as a simple example of a machine-learning potential that can achieve near-first-principles accuracy for many different metallurgically-important aspects of 2xxx alloys. High fidelity predictions of intermetallic compounds, elastic constants, dilute solid-solution energetics, precipitate/matrix interfaces, generalized stacking fault energies and surfaces for slip in matrix and precipitates, antisite defect energies, and other quantities, are shown. The NNPs also captures the subtle entropically-induced transition between and at temperatures around 600K. Many comparisons are made with the state-of-the-art Angular-Dependent Potential for Al-Cu, demonstrating the significant quantitative benefit of a machine-learning approach. A preliminary kinetic Monte Carlo study shows the NNP to predict the emergence of GP zones in Al-4at%Cu at T=300K in agreement with experiments.
Then, a family of NNPs for the Al-Mg-Si system is developed to enable quantitative studies of 6xxx alloys. The NNP is trained on metallurgically-important quantities computed by first principles density function theory (DFT) leading to high fidelity predictions of intermetallic compounds, elastic constants, dilute solid-solution energetics, precipitate/matrix interfaces, Al stacking fault energies, antisite defect energies, and other quantities. The Generalized Stacking Fault Energy surfaces (GSFE) for the three prevalent precipitate compositions in peak-aged Al-6xxx are then computed with the NNP, and are validated by DFT computations at key points. A preliminary examination of early-stage clustering kinetics and energetics in Al-6xxx is then made, showing the formation of low-energy Mg-Si structures and the trapping of vacancies in these clusters.
Finally we demonstrate a family of NNPs to produce what we believe to be the first Al-Cu-Mg (2xxx) and Al-Cu-Mg-Zn (7xxx) potentials of sufficient accuracy to be used for atomistic simulations.
These NNP studies show that the NNP has significant transferability to defects and properties outside the structures used to train the NNP, but